MSE is the Mean Square Error and is a model quality metric. Closer to zero is better. The MSE metric measures the average of the squares of the errors or deviations. MSE takes the distances from the points to the regression line (these distances are the “errors”) and then squares them to remove any negative signs. MSE incorporates both the variance and the bias of the predictor. MSE gives more weight to larger differences in errors than MAE.